Testing and Troubleshooting
Understand continuous monitoring and maintenance of AI/ML models to ensure long-term success.
Continuous maintenance
This section will build continuous maintenance, which includes continuous integration, continuous delivery, continuous training, and continuous monitoring, and expand on how to test and troubleshoot issues related to ML products on an ongoing basis so that our product is set up for success. Once we’ve made our first deployment, we jump right into the continuous training and continuous maintenance portion of the continuous maintenance process.
What is the significance of continuous maintenance in AI/ML product development?
Model maintenance
Remember, managing the performance of our models post-deployment is crucial, and it will be a highly iterative, never-ending process of model maintenance. As is the case with traditional software development, we will continue to test, troubleshoot, and fix bugs for our AI/ML products as well. The only difference is that we will also screen for lags in performance and bugs related to our model.
Match the answers
Match the following options with their description.
Continuous Integration (CI)
Ongoing retraining and maintenance of machine learning models.
Continuous Deployment (CD)
A process and technology for detecting compliance and risk issues associated with the operational environment.
Continuous Training (CT)
A process for automating the merging of code changes from multiple contributors into a shared repository.
Continuous Monitoring (CM)
A process in which every change that passes all stages of the production pipeline is released.
Model monitoring
Continuously monitoring our model makes sure that it’s always working properly and that the outputs it generates are effective. The last thing we want is for our product to be ...